Antenna Season Report Notebook¶

Josh Dillon, Last Revised January 2022

This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.

In [1]:
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
In [2]:
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
In [3]:
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "189"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
In [4]:
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))

Antenna 189 Report

In [5]:
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
In [6]:
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 32 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 30 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
In [7]:
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'

Load relevant info from summary CSVs¶

In [8]:
this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break
In [9]:
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])

Table 1: Per-Night RTP Summary Info For This Atenna¶

This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.

In [10]:
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))

Antenna 189, Node N15:

Out[10]:
JDs A Priori Status Auto Metrics Flags Dead Fraction in Ant Metrics (Jee) Dead Fraction in Ant Metrics (Jnn) Crossed Fraction in Ant Metrics Flag Fraction Before Redcal Flagged By Redcal chi^2 Fraction ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score Average Dead Ant Metric (Jee) Average Dead Ant Metric (Jnn) Average Crossed Ant Metric Median chi^2 Per Antenna (Jee) Median chi^2 Per Antenna (Jnn)
2459848 digital_ok 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 2.105716 2.872827 -0.146041 0.869342 -0.369141 2.143010 0.124684 8.689921 0.7146 0.7473 0.3857 3.765350 3.606285
2459846 digital_ok 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 2.893255 3.185744 0.022831 0.979814 0.147067 1.396093 0.023126 5.004374 0.8187 0.6584 0.5144 4.796044 4.730632
2459845 digital_ok 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 2.720218 3.414785 0.554330 -0.173870 0.101983 2.945502 0.781380 7.245660 0.7321 0.7397 0.3820 0.000000 0.000000
2459844 digital_ok 100.00% 100.00% 100.00% 0.00% - - 3.216871 1.908770 2.375983 -0.459974 32.875180 0.343954 76.384245 7.124932 0.0281 0.0247 0.0018 nan nan
2459843 digital_ok 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% nan nan inf inf nan nan nan nan nan nan nan 0.000000 0.000000
2459842 digital_ok 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.502960 1.105843 0.580816 0.497726 0.240607 0.572464 0.103856 0.274021 0.7540 0.6851 0.2541 2.145668 2.010638
2459841 digital_ok 100.00% 100.00% 100.00% 0.00% - - 1.435284 0.297930 0.412723 -0.746617 13.104742 12.726538 16.856012 1.662290 0.0273 0.0250 0.0017 nan nan
2459839 digital_ok 100.00% - - - - - 1.182399 0.807041 2.784200 -0.702259 2.712967 -0.067995 12.361963 0.989085 nan nan nan nan nan
2459838 digital_ok 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 2.010303 2.810625 0.229773 0.287804 1.189861 1.067031 0.012972 2.089925 0.7208 0.6955 0.4260 0.000000 0.000000
2459836 digital_ok - 100.00% 100.00% 0.00% - - nan nan nan nan nan nan nan nan 0.0388 0.0513 0.0040 nan nan
2459835 digital_ok 100.00% 100.00% 100.00% 0.00% - - -1.177426 -1.332406 -0.526807 -0.462404 3.839438 0.309132 20.972032 3.165402 0.0420 0.0504 0.0045 nan nan
2459833 digital_ok 100.00% 100.00% 100.00% 0.00% - - 1.917186 1.490770 -0.447635 0.628087 17.605622 -1.099215 72.383571 1.630558 0.0313 0.0433 0.0040 nan nan
2459832 digital_ok 0.00% 0.00% 0.00% 0.00% 0.00% 23.68% 3.753033 3.531793 -0.206215 0.631335 -0.175739 3.124388 0.474914 2.781092 0.8001 0.5039 0.6078 2.319072 1.603696
2459831 digital_ok 100.00% 100.00% 100.00% 0.00% - - 2.549766 0.939170 4.684641 -0.515526 5.457682 -0.397487 66.748647 0.370859 0.0267 0.0262 0.0024 nan nan
2459830 digital_ok 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 3.987328 4.055430 0.188432 0.793732 1.011772 1.725211 0.762745 7.800304 0.7979 0.5090 0.5932 5.306634 4.235489
2459829 digital_ok 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 3.995157 5.076919 0.915698 0.155355 0.795978 1.339233 2.209296 13.726266 0.7313 0.6447 0.4505 0.000000 0.000000
2459828 digital_ok 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 4.015717 4.129082 0.678516 -0.360235 1.186314 1.033388 1.842084 14.897083 0.7944 0.5247 0.5662 4.782985 2.273110
2459827 digital_ok 0.00% 0.00% 0.00% 0.00% 0.00% 3.74% 3.166218 3.592974 0.789109 -0.356675 0.115650 3.219561 1.794390 1.087516 0.7461 0.6674 0.4365 2.056275 1.635583
2459826 digital_ok 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 3.765493 3.761849 0.331993 0.431053 1.388266 1.341506 2.579832 5.955987 0.7919 0.5520 0.5379 0.000000 0.000000
2459825 digital_ok 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 3.278984 3.589165 0.429375 0.480582 9.910374 9.566571 5.244084 5.503051 0.7872 0.5504 0.5409 0.000000 0.000000
2459824 digital_ok 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 2.197612 2.966783 0.660662 0.280941 9.882396 10.998803 19.580320 18.474800 0.6801 0.7190 0.3992 0.000000 0.000000
2459823 digital_ok 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 4.116796 5.014118 0.277618 0.969141 -0.175741 1.410753 2.502187 5.448068 0.7307 0.6184 0.4991 0.000000 0.000000
2459822 digital_ok 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 3.674463 4.272980 0.727934 0.375975 6.383257 5.994941 4.652701 4.154288 0.7863 0.5740 0.5281 0.000000 0.000000
2459821 digital_ok 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 4.719574 5.342904 0.753408 -0.363291 3.586654 3.880793 3.477883 3.905682 0.7687 0.5682 0.5270 6.546456 6.572079
2459820 digital_ok 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 3.365759 3.882812 0.759053 -0.607441 2.522979 0.675605 0.679951 7.843229 0.7520 0.6679 0.4389 0.000000 0.000000
2459817 digital_ok 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 4.376551 4.717122 0.567484 -0.576622 1.127734 0.118834 2.390490 2.365694 0.7732 0.6037 0.5220 0.818248 0.823667
2459816 digital_ok 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 2.792466 3.061501 0.544485 2.130892 0.989717 1.419890 2.071712 20.118274 0.8305 0.5671 0.6101 4.901379 3.648693
2459815 digital_ok 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 3.780898 4.268780 0.428453 1.764857 0.300785 1.043468 1.433694 20.554176 0.7552 0.5926 0.5371 4.873546 3.757913
2459814 digital_ok 0.00% - - - - - nan nan nan nan nan nan nan nan nan nan nan nan nan
2459813 digital_ok 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% nan nan inf inf nan nan nan nan nan nan nan 0.000000 0.000000

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

In [11]:
htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.

In [12]:
for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

Antenna 189: 2459848

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
189 N15 digital_ok nn Temporal Discontinuties 8.689921 2.872827 2.105716 0.869342 -0.146041 2.143010 -0.369141 8.689921 0.124684

Antenna 189: 2459846

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
189 N15 digital_ok nn Temporal Discontinuties 5.004374 2.893255 3.185744 0.022831 0.979814 0.147067 1.396093 0.023126 5.004374

Antenna 189: 2459845

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
189 N15 digital_ok nn Temporal Discontinuties 7.245660 3.414785 2.720218 -0.173870 0.554330 2.945502 0.101983 7.245660 0.781380

Antenna 189: 2459844

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
189 N15 digital_ok ee Temporal Discontinuties 76.384245 3.216871 1.908770 2.375983 -0.459974 32.875180 0.343954 76.384245 7.124932

Antenna 189: 2459843

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
189 N15 digital_ok nn Shape nan nan nan inf inf nan nan nan nan

Antenna 189: 2459842

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
189 N15 digital_ok nn Shape 1.105843 0.502960 1.105843 0.580816 0.497726 0.240607 0.572464 0.103856 0.274021

Antenna 189: 2459841

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
189 N15 digital_ok ee Temporal Discontinuties 16.856012 1.435284 0.297930 0.412723 -0.746617 13.104742 12.726538 16.856012 1.662290

Antenna 189: 2459839

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
189 N15 digital_ok ee Temporal Discontinuties 12.361963 0.807041 1.182399 -0.702259 2.784200 -0.067995 2.712967 0.989085 12.361963

Antenna 189: 2459838

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
189 N15 digital_ok nn Shape 2.810625 2.810625 2.010303 0.287804 0.229773 1.067031 1.189861 2.089925 0.012972

Antenna 189: 2459835

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
189 N15 digital_ok ee Temporal Discontinuties 20.972032 -1.332406 -1.177426 -0.462404 -0.526807 0.309132 3.839438 3.165402 20.972032

Antenna 189: 2459833

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
189 N15 digital_ok ee Temporal Discontinuties 72.383571 1.490770 1.917186 0.628087 -0.447635 -1.099215 17.605622 1.630558 72.383571

Antenna 189: 2459832

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
189 N15 digital_ok ee Shape 3.753033 3.753033 3.531793 -0.206215 0.631335 -0.175739 3.124388 0.474914 2.781092

Antenna 189: 2459831

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
189 N15 digital_ok ee Temporal Discontinuties 66.748647 2.549766 0.939170 4.684641 -0.515526 5.457682 -0.397487 66.748647 0.370859

Antenna 189: 2459830

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
189 N15 digital_ok nn Temporal Discontinuties 7.800304 3.987328 4.055430 0.188432 0.793732 1.011772 1.725211 0.762745 7.800304

Antenna 189: 2459829

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
189 N15 digital_ok nn Temporal Discontinuties 13.726266 5.076919 3.995157 0.155355 0.915698 1.339233 0.795978 13.726266 2.209296

Antenna 189: 2459828

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
189 N15 digital_ok nn Temporal Discontinuties 14.897083 4.129082 4.015717 -0.360235 0.678516 1.033388 1.186314 14.897083 1.842084

Antenna 189: 2459827

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
189 N15 digital_ok nn Shape 3.592974 3.166218 3.592974 0.789109 -0.356675 0.115650 3.219561 1.794390 1.087516

Antenna 189: 2459826

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
189 N15 digital_ok nn Temporal Discontinuties 5.955987 3.761849 3.765493 0.431053 0.331993 1.341506 1.388266 5.955987 2.579832

Antenna 189: 2459825

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
189 N15 digital_ok ee Temporal Variability 9.910374 3.589165 3.278984 0.480582 0.429375 9.566571 9.910374 5.503051 5.244084

Antenna 189: 2459824

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
189 N15 digital_ok ee Temporal Discontinuties 19.580320 2.197612 2.966783 0.660662 0.280941 9.882396 10.998803 19.580320 18.474800

Antenna 189: 2459823

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
189 N15 digital_ok nn Temporal Discontinuties 5.448068 5.014118 4.116796 0.969141 0.277618 1.410753 -0.175741 5.448068 2.502187

Antenna 189: 2459822

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
189 N15 digital_ok ee Temporal Variability 6.383257 3.674463 4.272980 0.727934 0.375975 6.383257 5.994941 4.652701 4.154288

Antenna 189: 2459821

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
189 N15 digital_ok nn Shape 5.342904 5.342904 4.719574 -0.363291 0.753408 3.880793 3.586654 3.905682 3.477883

Antenna 189: 2459820

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
189 N15 digital_ok nn Temporal Discontinuties 7.843229 3.365759 3.882812 0.759053 -0.607441 2.522979 0.675605 0.679951 7.843229

Antenna 189: 2459817

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
189 N15 digital_ok nn Shape 4.717122 4.376551 4.717122 0.567484 -0.576622 1.127734 0.118834 2.390490 2.365694

Antenna 189: 2459816

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
189 N15 digital_ok nn Temporal Discontinuties 20.118274 3.061501 2.792466 2.130892 0.544485 1.419890 0.989717 20.118274 2.071712

Antenna 189: 2459815

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
189 N15 digital_ok nn Temporal Discontinuties 20.554176 4.268780 3.780898 1.764857 0.428453 1.043468 0.300785 20.554176 1.433694

Antenna 189: 2459814

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
189 N15 digital_ok nn Shape nan nan nan nan nan nan nan nan nan

Antenna 189: 2459813

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
189 N15 digital_ok nn Shape nan nan nan inf inf nan nan nan nan
In [ ]: